arXiv:2606.00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a common structural pattern in navigation, graph, and hierarchical decision problems: interface states such as doors, hubs, and bottlenecks naturally participate in more than one region. We introduce \emph{tangle-core abstraction}, an overlapping state-abstraction framework based on graph tangles of empirical transition graphs. The method constructs abstract states from consistently oriented low-order
This research addresses a fundamental limitation in current reinforcement learning state abstraction, particularly relevant as AI systems tackle increasingly complex, real-world navigation and decision tasks.
Improved state abstraction can lead to more efficient and robust AI agents, enabling them to generalize better and learn faster in complex environments.
The introduction of tangle-core abstraction offers a new approach to representing complex state spaces, potentially making AI more adaptable to hierarchical and graph-based problems.
- · AI/ML researchers
- · Robotics companies
- · Logistics and automation sectors
- · Reinforcement learning platforms
- · Systems relying on naive state partitioning
- · AI training with excessively long iteration cycles
- · Simple heuristic-based decision systems
AI agents become more capable at navigating and making decisions in complex, multi-region environments.
This improved capability accelerates the deployment of AI in physical world applications requiring sophisticated spatial or hierarchical reasoning.
More efficient and generalisable AI agents contribute to broader automation and potentially more autonomous systems in various industries, impacting labor dynamics and productivity.
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Read at arXiv cs.LG